High-res multi-view results

This table lists the benchmark results for the high-res multi-view scenario. The following metrics are evaluated:

Accuracy [%]: The fraction of the reconstruction which is closer to the ground truth than the evaluation threshold distance (*). Larger is better.
Completeness [%]: The fraction of the ground truth which is closer to the reconstruction than the evaluation threshold distance (*). Larger is better.
F₁ score [%]: The harmonic mean of accuracy and completeness, used to rank methods based on both metrics. Larger is better.
Time [s]: The runtime of the method. Smaller is better.

(*) For exact definitions, detailing how potentially incomplete ground truth is taken into account, see our paper.

The datasets are grouped into different categories, and result averages are computed for a category and method if results of the method are available for all datasets within the category. Note that the category "all" includes both the high-res multi-view and the low-res many-view scenarios.

Methods with suffix _ROB may participate in the Robust Vision Challenge.

Click a dataset result cell to show a visualization of the reconstruction. For training datasets, ground truth and accuracy / completeness visualizations are also available. The visualizations may not work with mobile browsers.

Method	all	high-res multi-view	indoor	outdoor	botani.	boulde.	bridge	door	exhibi.	lectur.	living.	lounge	observ.	old co.	statue	terrac.

MCST-MVS		83.01 2	83.30 3	82.15 10	92.84 2	57.58 15	93.77 1	91.10 6	75.95 2	77.10 24	93.09 17	54.15 23	95.57 10	85.80 3	85.87 34	93.31 9

DELS-MVS		82.32 4	82.50 4	81.79 11	86.80 41	56.89 25	92.77 2	86.30 78	77.83 1	77.88 21	92.13 32	60.24 4	94.78 18	78.97 25	89.61 6	93.69 6
Christian Sormann, Emanuele Santellani, Mattia Rossi, Andreas Kuhn, Friedrich Fraundorfer: DELS-MVS: Deep Epipolar Line Search for Multi-View Stereo. Winter Conference on Applications of Computer Vision (WACV), 2023
SED-MVS		81.92 6	81.75 7	82.44 7	89.48 14	56.89 25	92.48 3	88.94 27	57.50 198	84.61 3	94.09 4	58.81 9	96.37 5	82.28 11	87.60 16	94.06 3

OPU-MVS				81.71 12	92.05 3	55.97 41	92.48 3				94.01 6	67.21 2	95.10 13	89.82 1	92.55 3	94.06 3

DVP-MVS		81.30 8	81.00 10	82.20 8	89.40 17	57.45 17	92.34 5	89.99 14	57.71 192	81.91 9	93.64 10	55.17 17	96.44 2	81.68 13	87.13 19	92.72 21

test_0728_SECOND				82.20 8	89.50 12	57.73 12	92.34 5	88.88 29					96.39 4	81.68 13	87.13 19	92.47 24

test0726					89.40 17	57.45 17	92.32 7	88.63 37	57.71 192	83.14 7	93.96 7	55.17 17

DPM-MVS		82.39 3	82.36 5	82.49 5	80.12 177	59.50 5	92.24 8	90.72 8	69.37 22	83.22 6	94.47 2	63.81 3	93.18 30	74.02 61	93.25 2	94.80 1

ETH3 D test6400		83.28 1	83.47 1	82.72 3	91.48 4	59.33 6	92.10 9	90.95 7	65.68 57	80.67 15	94.42 3	59.41 7	95.89 9	86.74 2	89.75 5	92.94 16

CNVR-MVS		81.76 7	81.90 6	81.33 15	90.04 7	57.70 13	91.71 10	88.87 30	70.31 16	77.64 23	93.87 8	52.58 30	93.91 24	84.17 4	87.92 14	92.39 26

PS-MVSNAJ		80.06 13	79.52 14	81.68 13	85.58 55	60.97 3	91.69 11	87.02 64	70.62 13	80.75 14	93.22 14	37.77 173	92.50 42	82.75 8	86.25 31	91.57 46

xiu_mvs_v2_base		79.86 14	79.31 15	81.53 14	85.03 71	60.73 4	91.65 12	86.86 67	70.30 17	80.77 13	93.07 18	37.63 178	92.28 49	82.73 9	85.71 35	91.57 46

CANet		80.90 9	81.17 9	80.09 28	87.62 34	54.21 84	91.60 13	86.47 74	73.13 5	79.89 18	93.10 15	49.88 50	92.98 31	84.09 5	84.75 47	93.08 14

lupinMVS		78.38 22	78.11 24	79.19 37	83.02 113	55.24 54	91.57 14	84.82 115	69.12 23	76.67 26	92.02 36	44.82 101	90.23 98	80.83 18	80.09 84	92.08 33

NCCC		79.57 16	79.23 16	80.59 18	89.50 12	56.99 23	91.38 15	88.17 46	67.71 36	73.81 44	92.75 21	46.88 72	93.28 28	78.79 26	84.07 53	91.50 50

test_yl		75.85 61	74.83 65	78.91 42	88.08 30	51.94 145	91.30 16	89.28 18	57.91 186	71.19 75	89.20 100	42.03 135	92.77 35	69.41 85	75.07 127	92.01 36

DCV-MVSNet		75.85 61	74.83 65	78.91 42	88.08 30	51.94 145	91.30 16	89.28 18	57.91 186	71.19 75	89.20 100	42.03 135	92.77 35	69.41 85	75.07 127	92.01 36

LFMVS		78.52 19	77.14 35	82.67 4	89.58 10	58.90 8	91.27 18	88.05 47	63.22 91	74.63 36	90.83 63	41.38 143	94.40 19	75.42 51	79.90 89	94.72 2

VDD-MVS		76.08 57	74.97 62	79.44 33	84.27 83	53.33 111	91.13 19	85.88 86	65.33 64	72.37 63	89.34 97	32.52 235	92.76 37	77.90 36	75.96 116	92.22 31

DeepPCF-MVS	69.37 1	80.65 10	81.56 8	77.94 79	85.46 61	49.56 198	90.99 20	86.66 72	70.58 14	80.07 17	95.30 1	56.18 15	90.97 76	82.57 10	86.22 32	93.28 10

VNet		77.99 30	77.92 27	78.19 70	87.43 35	50.12 188	90.93 21	91.41 4	67.48 39	75.12 32	90.15 82	46.77 74	91.00 74	73.52 66	78.46 98	93.44 7

CLD-MVS		75.60 65	75.39 56	76.24 115	80.69 169	52.40 135	90.69 22	86.20 81	74.40 3	65.01 125	88.93 104	42.05 134	90.58 86	76.57 42	73.96 133	85.73 168
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
jason		77.01 42	76.45 44	78.69 54	79.69 180	54.74 71	90.56 23	83.99 138	68.26 28	74.10 42	90.91 60	42.14 132	89.99 102	79.30 24	79.12 92	91.36 54
jason: jason.
IB-MVS	68.87 2	74.01 83	72.03 98	79.94 29	83.04 112	55.50 46	90.24 24	88.65 35	67.14 41	61.38 165	81.74 199	53.21 26	94.28 20	60.45 154	62.41 223	90.03 86
Christian Sormann, Mattia Rossi, Andreas Kuhn and Friedrich Fraundorfer: IB-MVS: An Iterative Algorithm for Deep Multi-View Stereo based on Binary Decisions. BMVC 2021
VDDNet		74.37 78	72.13 94	81.09 17	79.58 181	56.52 31	90.02 25	86.70 71	52.61 250	71.23 74	87.20 131	31.75 245	93.96 23	74.30 59	75.77 119	92.79 20

TSAR-MVS + GP.		77.82 31	77.59 30	78.49 59	85.25 67	50.27 186	90.02 25	90.57 9	56.58 215	74.26 41	91.60 46	54.26 21	92.16 51	75.87 45	79.91 88	93.05 15

DeepC-MVS_fast	67.50 3	78.00 29	77.63 29	79.13 39	88.52 23	55.12 59	89.95 27	85.98 85	68.31 27	71.33 73	92.75 21	45.52 91	90.37 91	71.15 78	85.14 43	91.91 38
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
HPM-MVS++		80.50 11	80.71 11	79.88 30	87.34 36	55.20 57	89.93 28	87.55 59	66.04 56	79.46 19	93.00 19	53.10 27	91.76 58	80.40 20	89.56 7	92.68 22

MG-MVS		78.42 21	76.99 37	82.73 2	93.17 1	64.46 1	89.93 28	88.51 43	64.83 70	73.52 49	88.09 119	48.07 59	92.19 50	62.24 135	84.53 49	91.53 48

VPNet		72.07 111	71.42 105	74.04 161	78.64 199	47.17 246	89.91 30	87.97 48	72.56 7	64.66 128	85.04 156	41.83 139	88.33 150	61.17 143	60.97 230	86.62 153

alignmvs		78.08 28	77.98 25	78.39 65	83.53 96	53.22 114	89.77 31	85.45 91	66.11 51	76.59 28	91.99 38	54.07 24	89.05 121	77.34 39	77.00 108	92.89 18

APDe-MVS		78.44 20	78.20 21	79.19 37	88.56 22	54.55 79	89.76 32	87.77 54	55.91 221	78.56 20	92.49 25	48.20 58	92.65 40	79.49 22	83.04 57	90.39 77

SteuartSystems-ACMMP		77.08 41	76.33 46	79.34 35	80.98 160	55.31 52	89.76 32	86.91 66	62.94 95	71.65 68	91.56 47	42.33 128	92.56 41	77.14 40	83.69 55	90.15 84
Skip Steuart: Steuart Systems R&D Blog.
Anonymous202405211		70.11 135	67.88 150	76.79 108	87.20 39	47.24 245	89.49 34	77.38 250	54.88 233	66.14 109	86.84 137	20.93 312	91.54 62	56.45 191	71.62 155	91.59 44

DP-MVS Recon		71.99 112	70.31 116	77.01 100	90.65 6	53.44 104	89.37 35	82.97 157	56.33 218	63.56 148	89.47 94	34.02 220	92.15 53	54.05 203	72.41 149	85.43 175

EPNet		78.36 23	78.49 19	77.97 77	85.49 58	52.04 143	89.36 36	84.07 135	73.22 4	77.03 25	91.72 43	49.32 54	90.17 100	73.46 67	82.77 58	91.69 41
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
xxxxxxxxxxxxxcwj		77.31 38	76.54 41	79.61 31	85.35 63	56.34 35	89.31 37	72.84 294	61.55 116	74.63 36	92.38 27	47.75 63	91.35 67	78.18 33	86.85 24	91.15 58

save fliter					85.35 63	56.34 35	89.31 37	81.46 177	61.55 116

CSCG		80.41 12	79.72 12	82.49 5	89.12 21	57.67 14	89.29 39	91.54 3	59.19 159	71.82 67	90.05 84	59.72 6	96.04 7	78.37 28	88.40 12	93.75 5

MAR-MVS		76.76 48	75.60 53	80.21 23	90.87 5	54.68 75	89.14 40	89.11 22	62.95 94	70.54 79	92.33 29	41.05 144	94.95 15	57.90 178	86.55 28	91.00 64
Zhenyu Xu, Yiguang Liu, Xuelei Shi, Ying Wang, Yunan Zheng: MARMVS: Matching Ambiguity Reduced Multiple View Stereo for Efficient Large Scale Scene Reconstruction. CVPR 2020
CS-MVS		78.19 26	77.97 26	78.82 47	83.52 97	53.08 119	89.10 41	86.30 78	68.01 32	73.57 46	91.26 52	47.28 68	92.35 47	78.21 30	84.51 50	91.05 61

test_prior3		77.59 34	77.33 34	78.39 65	86.35 44	54.91 68	89.04 42	85.45 91	61.88 111	73.55 47	91.46 50	48.01 61	89.70 109	74.73 55	85.46 38	90.55 72

test_prior2							89.04 42		61.88 111	73.55 47	91.46 50	48.01 61		74.73 55	85.46 38

ET-MVSNet_ETH3D		75.23 69	74.08 72	78.67 55	84.52 77	55.59 44	88.92 44	89.21 20	68.06 31	53.13 261	90.22 78	49.71 51	87.62 173	72.12 74	70.82 162	92.82 19

PVSNet_Blended		76.53 51	76.54 41	76.50 110	85.91 47	51.83 150	88.89 45	84.24 132	67.82 34	69.09 84	89.33 99	46.70 75	88.13 157	75.43 49	81.48 71	89.55 94

Anonymous20240529		69.71 145	67.28 164	77.00 101	83.78 93	50.36 180	88.87 46	85.10 109	47.22 280	64.03 139	83.37 174	27.93 268	92.10 54	57.78 180	67.44 183	88.53 120

testtj		76.96 43	76.48 43	78.40 64	89.89 9	53.67 94	88.72 47	86.15 82	54.56 237	74.86 34	92.31 30	44.38 106	91.97 56	75.19 53	82.24 63	89.54 95

DPE-MVS		79.82 15	79.66 13	80.29 21	89.27 20	55.08 62	88.70 48	87.92 50	55.55 226	81.21 12	93.69 9	56.51 14	94.27 21	78.36 29	85.70 36	91.51 49

RRT_test8_iter05		72.74 98	71.20 108	77.36 90	87.25 38	53.51 98	88.68 49	89.53 16	65.20 67	61.32 166	81.27 203	45.89 85	92.48 44	65.99 108	55.65 279	86.10 161

PHI-MVS		77.49 35	77.00 36	78.95 41	85.33 65	50.69 170	88.57 50	88.59 41	58.14 181	73.60 45	93.31 12	43.14 124	93.79 25	73.81 62	88.53 11	92.37 27

WTY-MVS		77.47 36	77.52 31	77.30 92	88.33 26	46.25 258	88.46 51	90.32 10	71.40 10	72.32 64	91.72 43	53.44 25	92.37 46	66.28 107	75.42 122	93.28 10

ETH3D-3000-0.1		78.73 18	78.71 18	78.78 51	85.58 55	52.40 135	88.42 52	89.03 24	60.01 140	76.06 29	92.80 20	48.34 56	92.88 33	81.66 15	86.48 29	91.04 62

9.14			78.19 22		85.67 52		88.32 53	88.84 31	59.89 142	74.58 39	92.62 24	46.80 73	92.66 39	81.40 17	85.62 37

MVS_111021_HR		76.39 53	75.38 57	79.42 34	85.33 65	56.47 32	88.15 54	84.97 111	65.15 68	66.06 111	89.88 87	43.79 112	92.16 51	75.03 54	80.03 87	89.64 93

MS-PatchMatch		72.34 106	71.26 106	75.61 128	82.38 132	55.55 45	88.00 55	89.95 15	65.38 62	56.51 238	80.74 209	32.28 238	92.89 32	57.95 177	88.10 13	78.39 274

HQP-NCC					79.02 188		88.00 55		65.45 58	64.48 132

ACMP_Plane					79.02 188		88.00 55		65.45 58	64.48 132

HQP-MVS		72.34 106	71.44 104	75.03 140	79.02 188	51.56 155	88.00 55	83.68 142	65.45 58	64.48 132	85.13 154	37.35 184	88.62 136	66.70 102	73.12 141	84.91 182

canonicalmvs		78.17 27	77.86 28	79.12 40	84.30 80	54.22 83	87.71 59	84.57 122	67.70 37	77.70 22	92.11 35	50.90 41	89.95 103	78.18 33	77.54 103	93.20 12

VPA-MVSNet		71.12 120	70.66 112	72.49 194	78.75 194	44.43 275	87.64 60	90.02 13	63.97 78	65.02 124	81.58 201	42.14 132	87.42 178	63.42 129	63.38 212	85.63 172

test_8					85.72 49	55.31 52	87.60 61	83.88 139	57.84 189	72.84 56	90.99 55	44.99 96	88.34 148

Regformer-1		77.80 32	77.44 32	78.88 44	87.78 32	52.44 134	87.60 61	90.08 12	68.86 24	72.49 62	91.79 40	47.69 65	94.90 16	73.57 65	77.05 105	89.31 98

Regformer-2		77.15 40	76.82 39	78.14 71	87.78 32	51.84 149	87.60 61	89.12 21	67.23 40	71.93 66	91.79 40	46.03 83	93.53 27	72.85 71	77.05 105	89.05 107

TEST9					85.68 50	55.42 48	87.59 64	84.00 136	57.72 191	72.99 53	90.98 56	44.87 99	88.58 138

train_agg		76.91 44	76.40 45	78.45 62	85.68 50	55.42 48	87.59 64	84.00 136	57.84 189	72.99 53	90.98 56	44.99 96	88.58 138	78.19 31	85.32 41	91.34 56

SMA-MVS		79.10 17	78.76 17	80.12 26	84.42 78	55.87 42	87.58 66	86.76 69	61.48 121	80.26 16	93.10 15	46.53 77	92.41 45	79.97 21	88.77 9	92.08 33
Yufeng Yin; Xiaoyan Liu; Zichao Zhang: SMA-MVS: Segmentation-Guided Multi-Scale Anchor Deformation Patch Multi-View Stereo. IEEE Transactions on Circuits and Systems for Video Technology
agg_prior1		76.68 50	76.24 48	78.00 75	85.64 53	54.92 66	87.55 67	83.61 145	57.99 185	72.53 60	91.05 53	45.36 92	88.10 159	77.76 37	84.68 48	90.99 65

plane_prior						49.57 196	87.43 68		64.57 72						72.84 145

TR-MVS		69.71 145	67.85 152	75.27 137	82.94 117	48.48 224	87.40 69	80.86 187	57.15 203	64.61 130	87.08 134	32.67 234	89.64 112	46.38 251	71.55 157	87.68 135

CDPH-MVS		76.05 58	75.19 58	78.62 57	86.51 43	54.98 65	87.32 70	84.59 121	58.62 175	70.75 77	90.85 62	43.10 126	90.63 85	70.50 81	84.51 50	90.24 80

3Dnovator+	62.71 7	72.29 108	70.50 113	77.65 84	83.40 101	51.29 164	87.32 70	86.40 76	59.01 167	58.49 205	88.32 114	32.40 236	91.27 69	57.04 186	82.15 66	90.38 78

API-MVS		74.17 81	72.07 96	80.49 19	90.02 8	58.55 9	87.30 72	84.27 129	57.51 197	65.77 116	87.77 126	41.61 141	95.97 8	51.71 220	82.63 59	86.94 145

BH-RMVSNet		70.08 137	68.01 147	76.27 114	84.21 84	51.22 166	87.29 73	79.33 215	58.96 170	63.63 147	86.77 138	33.29 229	90.30 96	44.63 259	73.96 133	87.30 142

MTMP							87.27 74	15.34 356

APD-MVS		76.15 56	75.68 51	77.54 86	88.52 23	53.44 104	87.26 75	85.03 110	53.79 241	74.91 33	91.68 45	43.80 111	90.31 94	74.36 58	81.82 67	88.87 111
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
EIA-MVS		75.92 60	75.18 59	78.13 72	85.14 68	51.60 154	87.17 76	85.32 98	64.69 71	68.56 87	90.53 70	45.79 88	91.58 61	67.21 99	82.18 65	91.20 57

test_prior4						56.39 34	87.15 77

casdiffmvs		77.36 37	76.85 38	78.88 44	80.40 175	54.66 77	87.06 78	85.88 86	72.11 8	71.57 70	88.63 112	50.89 43	90.35 92	76.00 44	79.11 93	91.63 43

cascas		69.01 157	66.13 184	77.66 83	79.36 182	55.41 50	86.99 79	83.75 141	56.69 212	58.92 195	81.35 202	24.31 292	92.10 54	53.23 206	70.61 163	85.46 174

nrg030		72.27 110	71.56 101	74.42 152	75.93 238	50.60 172	86.97 80	83.21 152	62.75 97	67.15 99	84.38 160	50.07 47	86.66 195	71.19 77	62.37 224	85.99 162

#test#		74.86 76	73.78 76	78.10 73	84.30 80	53.68 92	86.95 81	84.36 125	59.00 168	65.78 114	90.56 67	40.70 149	90.90 77	71.48 76	80.88 73	89.71 90

114514_t		69.87 143	67.88 150	75.85 126	88.38 25	52.35 139	86.94 82	83.68 142	53.70 242	55.68 244	85.60 150	30.07 257	91.20 70	55.84 193	71.02 160	83.99 194

CP-MVS		72.59 103	71.46 103	76.00 124	82.93 118	52.32 140	86.93 83	82.48 163	55.15 230	63.65 146	90.44 75	35.03 212	88.53 142	68.69 91	77.83 101	87.15 143

ZNCC-MVS		75.82 64	75.02 61	78.23 69	83.88 92	53.80 90	86.91 84	86.05 84	59.71 144	67.85 94	90.55 69	42.23 130	91.02 73	72.66 73	85.29 42	89.87 89

PAPM		76.76 48	76.07 50	78.81 48	80.20 176	59.11 7	86.86 85	86.23 80	68.60 25	70.18 81	88.84 107	51.57 35	87.16 182	65.48 114	86.68 26	90.15 84

Fast-Effi-MVS+		72.73 99	71.15 110	77.48 87	82.75 124	54.76 70	86.77 86	80.64 190	63.05 93	65.93 112	84.01 163	44.42 105	89.03 122	56.45 191	76.36 115	88.64 116

ETH3D cwj APD-0.16		78.36 23	78.19 22	78.86 46	84.21 84	52.68 128	86.70 87	89.02 25	59.13 164	75.37 31	92.49 25	49.06 55	93.20 29	80.67 19	87.08 21	90.71 70

thisisatest0515		73.64 89	72.20 92	77.97 77	81.63 143	53.01 122	86.69 88	88.81 32	62.53 100	64.06 138	85.65 149	52.15 33	92.50 42	58.43 167	69.84 168	88.39 122

SF-MVS		77.64 33	77.42 33	78.32 68	83.75 94	52.47 133	86.63 89	87.80 51	58.78 172	74.63 36	92.38 27	47.75 63	91.35 67	78.18 33	86.85 24	91.15 58

BH-w/o		70.02 139	68.51 140	74.56 148	82.77 122	50.39 179	86.60 90	78.14 236	59.77 143	59.65 180	85.57 151	39.27 162	87.30 180	49.86 229	74.94 129	85.99 162

mvs-test1		69.04 155	67.57 158	73.44 178	75.17 244	51.68 153	86.57 91	74.48 280	62.15 104	62.07 161	85.79 147	30.59 253	87.48 176	65.40 119	65.94 194	81.18 246

DeepC-MVS	67.15 4	76.90 46	76.27 47	78.80 49	80.70 168	55.02 63	86.39 92	86.71 70	66.96 43	67.91 93	89.97 86	48.03 60	91.41 66	75.60 48	84.14 52	89.96 87
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
TransMVSNet (Re)		62.82 235	60.76 236	69.02 250	73.98 260	41.61 298	86.36 93	79.30 216	56.90 205	52.53 264	76.44 251	41.85 138	87.60 174	38.83 275	40.61 326	77.86 280

MSLP-MVS++		74.21 80	72.25 91	80.11 27	81.45 154	56.47 32	86.32 94	79.65 206	58.19 180	66.36 107	92.29 31	36.11 201	90.66 83	67.39 97	82.49 60	93.18 13

QAPM		71.88 113	69.33 132	79.52 32	82.20 134	54.30 82	86.30 95	88.77 33	56.61 214	59.72 179	87.48 129	33.90 223	95.36 11	47.48 244	81.49 70	88.90 110

WR-MVS		67.58 183	66.76 172	70.04 242	75.92 239	45.06 271	86.23 96	85.28 101	64.31 74	58.50 204	81.00 204	44.80 103	82.00 260	49.21 234	55.57 280	83.06 216

PVSNet_BlendedMVS		73.42 91	73.30 78	73.76 170	85.91 47	51.83 150	86.18 97	84.24 132	65.40 61	69.09 84	80.86 207	46.70 75	88.13 157	75.43 49	65.92 195	81.33 242

ETV-MVS		77.17 39	76.74 40	78.48 60	81.80 139	54.55 79	86.13 98	85.33 97	68.20 29	73.10 52	90.52 71	45.23 94	90.66 83	79.37 23	80.95 72	90.22 81

AdaColmap		67.86 178	65.48 198	75.00 141	88.15 29	54.99 64	86.10 99	76.63 264	49.30 270	57.80 214	86.65 141	29.39 260	88.94 131	45.10 257	70.21 166	81.06 247

OPM-MVS		70.75 128	69.58 127	74.26 157	75.55 243	51.34 162	86.05 100	83.29 151	61.94 110	62.95 153	85.77 148	34.15 219	88.44 144	65.44 118	71.07 159	82.99 217

Vis-MVSNet		70.61 130	69.34 131	74.42 152	80.95 164	48.49 223	86.03 101	77.51 247	58.74 173	65.55 118	87.78 125	34.37 216	85.95 219	52.53 218	80.61 77	88.80 112
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
Anonymous20231211		66.08 215	63.67 218	73.31 180	83.07 111	48.75 216	86.01 102	84.67 120	45.27 297	56.54 236	76.67 249	28.06 267	88.95 129	52.78 213	59.95 233	82.23 225

test_part1		66.52 208	63.52 220	75.55 129	72.58 275	53.36 108	85.90 103	84.33 127	46.56 285	56.80 231	75.55 263	34.32 218	90.46 88	58.80 164	56.80 264	82.83 221

EG-PatchMatch MVS		62.40 242	59.59 244	70.81 229	73.29 265	49.05 207	85.81 104	84.78 117	51.85 257	44.19 300	73.48 279	15.52 332	89.85 104	40.16 272	67.24 184	73.54 312

PVSNet_Blended_VisFu		73.40 92	72.44 87	76.30 113	81.32 158	54.70 74	85.81 104	78.82 222	63.70 83	64.53 131	85.38 153	47.11 71	87.38 179	67.75 96	77.55 102	86.81 151

HQP_MVS		70.96 124	69.91 124	74.12 159	77.95 210	49.57 196	85.76 106	82.59 161	63.60 86	62.15 159	83.28 176	36.04 204	88.30 152	65.46 115	72.34 150	84.49 185

plane_prior2							85.76 106		63.60 86

GST-MVS		74.87 75	73.90 74	77.77 80	83.30 103	53.45 103	85.75 108	85.29 100	59.22 158	66.50 106	89.85 88	40.94 145	90.76 80	70.94 79	83.35 56	89.10 106

SD-MVS		76.18 55	74.85 64	80.18 24	85.39 62	56.90 24	85.75 108	82.45 164	56.79 210	74.48 40	91.81 39	43.72 115	90.75 81	74.61 57	78.65 96	92.91 17
Zhenlong Yuan, Jiakai Cao, Zhaoxin Li, Hao Jiang and Zhaoqi Wang: SD-MVS: Segmentation-driven Deformation Multi-View Stereo with Spherical Refinement and EM optimization. AAAI2024
CHOSEN 1792x2688		76.24 54	74.03 73	82.88 1	83.09 109	62.84 2	85.73 110	85.39 94	69.79 19	64.87 127	83.49 172	41.52 142	93.69 26	70.55 80	81.82 67	92.12 32

FMVSNet3		68.84 159	67.40 162	73.19 182	85.05 69	48.53 221	85.71 111	85.36 95	60.90 129	57.58 220	79.15 220	42.16 131	86.77 191	47.25 246	63.40 209	84.27 189

MP-MVS		74.99 74	74.33 70	76.95 103	82.89 119	53.05 121	85.63 112	83.50 147	57.86 188	67.25 98	90.24 77	43.38 121	88.85 133	76.03 43	82.23 64	88.96 109
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
HFP-MVS		74.37 78	73.13 81	78.10 73	84.30 80	53.68 92	85.58 113	84.36 125	56.82 208	65.78 114	90.56 67	40.70 149	90.90 77	69.18 88	80.88 73	89.71 90

ACMMPR		73.76 86	72.61 83	77.24 97	83.92 90	52.96 124	85.58 113	84.29 128	56.82 208	65.12 121	90.45 72	37.24 188	90.18 99	69.18 88	80.84 75	88.58 118

BH-untuned		68.28 172	66.40 177	73.91 164	81.62 144	50.01 190	85.56 115	77.39 249	57.63 194	57.47 225	83.69 170	36.36 199	87.08 184	44.81 258	73.08 144	84.65 184

region2R		73.75 87	72.55 85	77.33 91	83.90 91	52.98 123	85.54 116	84.09 134	56.83 207	65.10 122	90.45 72	37.34 186	90.24 97	68.89 90	80.83 76	88.77 114

xiu_mvs_v1_base_debu		71.60 116	70.29 117	75.55 129	77.26 221	53.15 115	85.34 117	79.37 209	55.83 222	72.54 57	90.19 79	22.38 302	86.66 195	73.28 68	76.39 112	86.85 148

xiu_mvs_v1_base		71.60 116	70.29 117	75.55 129	77.26 221	53.15 115	85.34 117	79.37 209	55.83 222	72.54 57	90.19 79	22.38 302	86.66 195	73.28 68	76.39 112	86.85 148

xiu_mvs_v1_base_debi		71.60 116	70.29 117	75.55 129	77.26 221	53.15 115	85.34 117	79.37 209	55.83 222	72.54 57	90.19 79	22.38 302	86.66 195	73.28 68	76.39 112	86.85 148

NR-MVSNet		67.25 193	65.99 187	71.04 226	73.27 266	43.91 279	85.32 120	84.75 118	66.05 55	53.65 259	82.11 195	45.05 95	85.97 218	47.55 243	56.18 271	83.24 211

Effi-MVS+		75.24 68	73.61 77	80.16 25	81.92 137	57.42 19	85.21 121	76.71 262	60.68 133	73.32 51	89.34 97	47.30 67	91.63 60	68.28 93	79.72 90	91.42 51

无先验							85.19 122	78.00 239	49.08 271				85.13 233	52.78 213		87.45 139

FMVSNet2		67.57 184	65.79 191	72.90 185	82.71 125	47.97 238	85.15 123	84.93 112	58.55 176	56.71 234	78.26 226	36.72 196	86.67 194	46.15 253	62.94 220	84.07 191

test-LLR		69.65 148	69.01 136	71.60 215	78.67 196	48.17 231	85.13 124	79.72 203	59.18 161	63.13 151	82.58 187	36.91 193	80.24 274	60.56 150	75.17 124	86.39 158

TESTMET0.1,1		72.86 97	72.33 88	74.46 150	81.98 136	50.77 168	85.13 124	85.47 90	66.09 52	67.30 96	83.69 170	37.27 187	83.57 249	65.06 122	78.97 95	89.05 107

test-mter		68.36 169	67.29 163	71.60 215	78.67 196	48.17 231	85.13 124	79.72 203	53.38 244	63.13 151	82.58 187	27.23 274	80.24 274	60.56 150	75.17 124	86.39 158

Regformer-3		76.02 59	75.47 55	77.70 82	85.49 58	51.47 158	85.12 127	90.19 11	68.52 26	69.36 82	90.66 65	46.45 78	94.81 17	70.25 83	73.16 139	86.81 151

Regformer-4		75.06 73	74.59 68	76.47 111	85.49 58	50.33 182	85.12 127	88.61 39	66.42 45	68.48 88	90.66 65	44.15 107	92.68 38	69.24 87	73.16 139	86.39 158

1112_ss		70.05 138	69.37 130	72.10 200	80.77 167	42.78 288	85.12 127	76.75 261	59.69 145	61.19 168	92.12 33	47.48 66	83.84 245	53.04 209	68.21 177	89.66 92

XXY-MVS		70.18 134	69.28 134	72.89 187	77.64 214	42.88 287	85.06 130	87.50 60	62.58 99	62.66 157	82.34 192	43.64 117	89.83 105	58.42 169	63.70 208	85.96 164

MSP-MVS		82.30 5	83.47 1	78.80 49	82.99 115	52.71 127	85.04 131	88.63 37	66.08 53	86.77 2	92.75 21	72.05 1	91.46 65	83.35 6	93.53 1	92.23 29

thres200		68.71 165	67.27 165	73.02 183	84.73 74	46.76 249	85.03 132	87.73 55	62.34 103	59.87 176	83.45 173	43.15 123	88.32 151	31.25 307	67.91 181	83.98 196

MVP-Stereo		70.97 123	70.44 114	72.59 191	76.03 237	51.36 161	85.02 133	86.99 65	60.31 137	56.53 237	78.92 222	40.11 155	90.00 101	60.00 159	90.01 4	76.41 294
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
PS-MVSNAJss		68.78 164	67.17 166	73.62 175	73.01 268	48.33 229	84.95 134	84.81 116	59.30 157	58.91 196	79.84 213	37.77 173	88.86 132	62.83 132	63.12 218	83.67 204

v2v482		69.55 151	67.64 155	75.26 138	72.32 279	53.83 89	84.93 135	81.94 168	65.37 63	60.80 170	79.25 218	41.62 140	88.98 128	63.03 131	59.51 237	82.98 218

XVS		72.92 96	71.62 100	76.81 105	83.41 98	52.48 131	84.88 136	83.20 153	58.03 182	63.91 141	89.63 92	35.50 208	89.78 106	65.50 112	80.50 79	88.16 123

X-MVStestdata		65.85 217	62.20 225	76.81 105	83.41 98	52.48 131	84.88 136	83.20 153	58.03 182	63.91 141	4.82 354	35.50 208	89.78 106	65.50 112	80.50 79	88.16 123

Fast-Effi-MVS+-dtu		66.53 207	64.10 216	73.84 167	72.41 277	52.30 141	84.73 138	75.66 271	59.51 148	56.34 239	79.11 221	28.11 266	85.85 221	57.74 181	63.29 213	83.35 207

v1144		68.81 161	66.82 169	74.80 146	72.34 278	53.46 100	84.68 139	81.77 174	64.25 75	60.28 175	77.91 228	40.23 152	88.95 129	60.37 155	59.52 236	81.97 227

CANet_DTU		73.71 88	73.14 79	75.40 133	82.61 129	50.05 189	84.67 140	79.36 212	69.72 20	75.39 30	90.03 85	29.41 259	85.93 220	67.99 95	79.11 93	90.22 81

PVSNet	62.49 8	69.27 153	67.81 153	73.64 173	84.41 79	51.85 148	84.63 141	77.80 241	66.42 45	59.80 178	84.95 157	22.14 306	80.44 272	55.03 196	75.11 126	88.62 117

mPP-MVS		71.79 115	70.38 115	76.04 122	82.65 128	52.06 142	84.45 142	81.78 173	55.59 225	62.05 162	89.68 91	33.48 227	88.28 154	65.45 117	78.24 100	87.77 133

OpenMVS	61.00 11	69.99 141	67.55 159	77.30 92	78.37 206	54.07 88	84.36 143	85.76 88	57.22 202	56.71 234	87.67 127	30.79 252	92.83 34	43.04 265	84.06 54	85.01 180

MP-MVS-pluss		75.54 66	75.03 60	77.04 98	81.37 156	52.65 130	84.34 144	84.46 123	61.16 124	69.14 83	91.76 42	39.98 157	88.99 127	78.19 31	84.89 46	89.48 96
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
PAPR		75.20 70	74.13 71	78.41 63	88.31 27	55.10 61	84.31 145	85.66 89	63.76 82	67.55 95	90.73 64	43.48 120	89.40 115	66.36 106	77.03 107	90.73 69

SR-MVS		70.92 125	69.73 126	74.50 149	83.38 102	50.48 176	84.27 146	79.35 213	48.96 273	66.57 105	90.45 72	33.65 226	87.11 183	66.42 104	74.56 130	85.91 165

v148		68.24 174	66.35 178	73.88 165	71.76 282	51.47 158	84.23 147	81.90 172	63.69 84	58.94 193	76.44 251	43.72 115	87.78 169	60.63 148	55.86 276	82.39 224

UniMVSNet_NR-MVSNet		68.82 160	68.29 144	70.40 235	75.71 241	42.59 290	84.23 147	86.78 68	66.31 47	58.51 202	82.45 189	51.57 35	84.64 241	53.11 207	55.96 274	83.96 198

v144192		67.86 178	65.76 192	74.16 158	71.68 283	53.09 118	84.14 149	80.83 188	62.85 96	59.21 190	77.28 238	39.30 161	88.00 162	58.67 166	57.88 258	81.40 239

UGNet		68.71 165	67.11 167	73.50 177	80.55 173	47.61 240	84.08 150	78.51 230	59.45 149	65.68 117	82.73 185	23.78 294	85.08 235	52.80 212	76.40 111	87.80 132
Wanjuan Su, Qingshan Xu, Wenbing Tao: Uncertainty-guided Multi-view Stereo Network for Depth Estimation. IEEE Transactions on Circuits and Systems for Video Technology, 2022
PMMVS		72.98 95	72.05 97	75.78 127	83.57 95	48.60 218	84.08 150	82.85 159	61.62 115	68.24 91	90.33 76	28.35 263	87.78 169	72.71 72	76.69 110	90.95 66

ACMMP_NAP		76.43 52	75.66 52	78.73 52	81.92 137	54.67 76	84.06 152	85.35 96	61.10 126	72.99 53	91.50 48	40.25 151	91.00 74	76.84 41	86.98 22	90.51 76

v1192		67.96 177	65.74 193	74.63 147	71.79 281	53.43 106	84.06 152	80.99 186	63.19 92	59.56 183	77.46 235	37.50 183	88.65 135	58.20 172	58.93 242	81.79 229

zzz-MVS		74.15 82	73.11 82	77.27 94	81.54 149	53.57 96	84.02 154	81.31 180	59.41 151	68.39 89	90.96 58	36.07 202	89.01 123	73.80 63	82.45 61	89.23 100

FIs		70.00 140	70.24 120	69.30 248	77.93 212	38.55 310	83.99 155	87.72 56	66.86 44	57.66 218	84.17 162	52.28 31	85.31 227	52.72 217	68.80 174	84.02 192

MVS_Test		75.85 61	74.93 63	78.62 57	84.08 86	55.20 57	83.99 155	85.17 105	68.07 30	73.38 50	82.76 182	50.44 45	89.00 125	65.90 110	80.61 77	91.64 42

baseline		76.86 47	76.24 48	78.71 53	80.47 174	54.20 86	83.90 157	84.88 114	71.38 11	71.51 71	89.15 102	50.51 44	90.55 87	75.71 46	78.65 96	91.39 52

baseline2		75.15 71	74.54 69	76.98 102	81.67 142	51.74 152	83.84 158	91.94 1	69.97 18	58.98 192	86.02 146	59.73 5	91.73 59	68.37 92	70.40 165	87.48 137

EPP-MVSNet		71.14 119	70.07 122	74.33 155	79.18 187	46.52 252	83.81 159	86.49 73	56.32 219	57.95 211	84.90 158	54.23 22	89.14 118	58.14 173	69.65 170	87.33 140

原ACMM2							83.77 160

v1921920		67.45 187	65.23 205	74.10 160	71.51 286	52.90 125	83.75 161	80.44 193	62.48 102	59.12 191	77.13 239	36.98 191	87.90 163	57.53 182	58.14 252	81.49 234

OpenMVS_ROB	53.19 17	59.20 257	56.00 266	68.83 253	71.13 291	44.30 276	83.64 162	75.02 277	46.42 289	46.48 296	73.03 282	18.69 319	88.14 156	27.74 320	61.80 226	74.05 308

MVSTER		73.25 93	72.33 88	76.01 123	85.54 57	53.76 91	83.52 163	87.16 62	67.06 42	63.88 143	81.66 200	52.77 28	90.44 89	64.66 123	64.69 200	83.84 201

GBi-Net		67.09 197	65.47 199	71.96 206	82.71 125	46.36 254	83.52 163	83.31 148	58.55 176	57.58 220	76.23 255	36.72 196	86.20 204	47.25 246	63.40 209	83.32 208

test1		67.09 197	65.47 199	71.96 206	82.71 125	46.36 254	83.52 163	83.31 148	58.55 176	57.58 220	76.23 255	36.72 196	86.20 204	47.25 246	63.40 209	83.32 208

FMVSNet1		64.57 221	62.11 226	71.96 206	77.32 219	46.36 254	83.52 163	83.31 148	52.43 252	54.42 251	76.23 255	27.80 270	86.20 204	42.59 269	61.34 229	83.32 208

baseline1		72.51 104	72.12 95	73.69 171	85.05 69	44.46 273	83.51 167	86.13 83	71.61 9	64.64 129	87.97 122	55.00 20	89.48 114	59.07 161	56.05 273	87.13 144

CDS-MVSNet		70.48 132	69.43 128	73.64 173	77.56 216	48.83 215	83.51 167	77.45 248	63.27 90	62.33 158	85.54 152	43.85 109	83.29 253	57.38 185	74.00 132	88.79 113
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
thisisatest0530		70.47 133	68.56 139	76.20 117	79.78 179	51.52 157	83.49 169	88.58 42	57.62 195	58.60 201	82.79 181	51.03 40	91.48 64	52.84 211	62.36 225	85.59 173

Test_1112_low_res		67.18 195	66.23 181	70.02 243	78.75 194	41.02 302	83.43 170	73.69 288	57.29 201	58.45 207	82.39 191	45.30 93	80.88 267	50.50 225	66.26 193	88.16 123

ACMMP		70.81 127	69.29 133	75.39 134	81.52 153	51.92 147	83.43 170	83.03 156	56.67 213	58.80 199	88.91 105	31.92 243	88.58 138	65.89 111	73.39 138	85.67 169
Qingshan Xu, Weihang Kong, Wenbing Tao, Marc Pollefeys: Multi-Scale Geometric Consistency Guided and Planar Prior Assisted Multi-View Stereo. IEEE Transactions on Pattern Analysis and Machine Intelligence
tfpn200view9		67.57 184	66.13 184	71.89 213	84.05 87	45.07 268	83.40 172	87.71 57	60.79 130	57.79 215	82.76 182	43.53 118	87.80 166	28.80 313	66.36 190	82.78 222

thres400		67.40 191	66.13 184	71.19 223	84.05 87	45.07 268	83.40 172	87.71 57	60.79 130	57.79 215	82.76 182	43.53 118	87.80 166	28.80 313	66.36 190	80.71 252

v1240		66.99 200	64.68 210	73.93 163	71.38 289	52.66 129	83.39 174	79.98 197	61.97 109	58.44 208	77.11 240	35.25 210	87.81 165	56.46 190	58.15 250	81.33 242

Baseline_NR-MVSNet		65.49 219	64.27 214	69.13 249	74.37 257	41.65 297	83.39 174	78.85 220	59.56 147	59.62 182	76.88 246	40.75 146	87.44 177	49.99 227	55.05 281	78.28 276

miper_enhance_ethall		69.77 144	68.90 137	72.38 196	78.93 191	49.91 192	83.29 176	78.85 220	64.90 69	59.37 186	79.46 215	52.77 28	85.16 232	63.78 126	58.72 243	82.08 226

diffmvs		75.11 72	74.65 67	76.46 112	78.52 202	53.35 109	83.28 177	79.94 198	70.51 15	71.64 69	88.72 108	46.02 84	86.08 214	77.52 38	75.75 120	89.96 87

ACMP	61.11 9	66.24 213	64.33 213	72.00 205	74.89 251	49.12 205	83.18 178	79.83 201	55.41 228	52.29 266	82.68 186	25.83 282	86.10 210	60.89 145	63.94 206	80.78 250
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
GA-MVS		69.04 155	66.70 174	76.06 121	75.11 246	52.36 138	83.12 179	80.23 194	63.32 89	60.65 172	79.22 219	30.98 251	88.37 146	61.25 142	66.41 189	87.46 138

3Dnovator	64.70 6	74.46 77	72.48 86	80.41 20	82.84 121	55.40 51	83.08 180	88.61 39	67.61 38	59.85 177	88.66 109	34.57 215	93.97 22	58.42 169	88.70 10	91.85 40

PGM-MVS		72.60 101	71.20 108	76.80 107	82.95 116	52.82 126	83.07 181	82.14 165	56.51 216	63.18 150	89.81 89	35.68 207	89.76 108	67.30 98	80.19 83	87.83 131

LPG-MVS_test		66.44 210	64.58 211	72.02 203	74.42 255	48.60 218	83.07 181	80.64 190	54.69 235	53.75 257	83.83 166	25.73 284	86.98 186	60.33 156	64.71 198	80.48 254

TranMVSNet+NR-MVSNet		66.94 202	65.61 196	70.93 228	73.45 263	43.38 284	83.02 183	84.25 130	65.31 65	58.33 209	81.90 198	39.92 158	85.52 223	49.43 232	54.89 283	83.89 200

test0.0.03 1		62.54 237	62.44 223	62.86 295	72.28 280	29.51 336	82.93 184	78.78 223	59.18 161	53.07 262	82.41 190	36.91 193	77.39 299	37.45 278	58.96 241	81.66 232

pm-mvs1		64.12 224	62.56 222	68.78 255	71.68 283	38.87 309	82.89 185	81.57 175	55.54 227	53.89 256	77.82 230	37.73 176	86.74 192	48.46 239	53.49 293	80.72 251

DWT-MVSNet_test		75.47 67	73.87 75	80.29 21	87.33 37	57.05 22	82.86 186	87.96 49	72.59 6	67.29 97	87.79 124	51.61 34	91.52 63	54.75 200	72.63 147	92.29 28

EPNet_dtu		66.25 212	66.71 173	64.87 285	78.66 198	34.12 322	82.80 187	75.51 272	61.75 113	64.47 135	86.90 136	37.06 190	72.46 323	43.65 263	69.63 171	88.02 129
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
test1172		69.64 149	68.38 143	73.41 179	82.77 122	48.84 214	82.79 188	78.34 234	47.02 283	65.27 120	90.07 83	31.17 249	86.09 212	64.51 124	73.49 137	85.31 176

pmmvs5		62.80 236	61.18 233	67.66 264	69.53 300	42.37 295	82.65 189	75.19 276	54.30 240	52.03 269	78.51 225	31.64 246	80.67 268	48.60 237	58.15 250	79.95 261

cl-mvsnet_		67.43 188	65.93 188	71.95 209	76.33 230	48.02 236	82.58 190	79.12 217	61.30 123	56.72 233	76.92 244	46.12 80	86.44 202	57.98 175	56.31 268	81.38 241

cl-mvsnet1		67.43 188	65.93 188	71.94 210	76.33 230	48.01 237	82.57 191	79.11 218	61.31 122	56.73 232	76.92 244	46.09 81	86.43 203	57.98 175	56.31 268	81.39 240

TAMVS		69.51 152	68.16 146	73.56 176	76.30 232	48.71 217	82.57 191	77.17 253	62.10 106	61.32 166	84.23 161	41.90 137	83.46 251	54.80 199	73.09 143	88.50 121

EI-MVSNet-Vis-set		73.19 94	72.60 84	74.99 142	82.56 130	49.80 194	82.55 193	89.00 26	66.17 50	65.89 113	88.98 103	43.83 110	92.29 48	65.38 121	69.01 173	82.87 220

DP-MVS		59.24 256	56.12 265	68.63 258	88.24 28	50.35 181	82.51 194	64.43 322	41.10 316	46.70 294	78.77 223	24.75 291	88.57 141	22.26 332	56.29 270	66.96 330

miper_ehance_all_eth		68.70 167	67.58 156	72.08 201	76.91 227	49.48 201	82.47 195	78.45 232	62.68 98	58.28 210	77.88 229	50.90 41	85.01 236	61.91 138	58.72 243	81.75 230

cl-mvsnet2		68.85 158	67.69 154	72.35 197	78.07 209	49.98 191	82.45 196	78.48 231	62.50 101	58.46 206	77.95 227	49.99 48	85.17 231	62.55 133	58.72 243	81.90 228

UniMVSNet (Re)		67.71 181	66.80 170	70.45 233	74.44 254	42.93 286	82.42 197	84.90 113	63.69 84	59.63 181	80.99 205	47.18 69	85.23 230	51.17 223	56.75 265	83.19 213

v8		67.25 193	64.99 208	74.04 161	72.89 271	53.31 112	82.37 198	80.11 196	61.54 118	54.29 253	76.02 260	42.89 127	88.41 145	58.43 167	56.36 266	80.39 256

RRT_MVS		65.43 220	64.01 217	69.68 245	81.54 149	50.15 187	82.31 199	76.78 260	55.25 229	60.64 173	82.00 197	25.18 287	79.00 284	60.96 144	51.45 299	79.89 262

ACMM	58.35 12	64.35 223	62.01 227	71.38 219	74.21 258	48.51 222	82.25 200	79.66 205	47.61 277	54.54 250	80.11 211	25.26 286	86.00 215	51.26 221	63.16 216	79.64 264
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
thres600view7		66.46 209	65.12 206	70.47 232	83.41 98	43.80 281	82.15 201	87.78 52	59.37 153	56.02 241	82.21 193	43.73 113	86.90 189	26.51 324	64.94 197	80.71 252

cl_fuxian		67.97 176	66.66 175	71.91 212	76.20 234	49.31 203	82.13 202	78.00 239	61.99 108	57.64 219	76.94 243	49.41 52	84.93 237	60.62 149	57.01 263	81.49 234

Effi-MVS+-dtu		66.24 213	64.96 209	70.08 240	75.17 244	49.64 195	82.01 203	74.48 280	62.15 104	57.83 213	76.08 259	30.59 253	83.79 246	65.40 119	60.93 231	76.81 289

our_test_3		59.11 259	55.08 273	71.18 224	71.42 287	53.29 113	81.96 204	74.52 279	48.32 274	42.08 309	69.28 306	28.14 265	82.15 257	34.35 295	45.68 317	78.11 279

CPTT-MVS		67.15 196	65.84 190	71.07 225	80.96 161	50.32 183	81.94 205	74.10 283	46.18 292	57.91 212	87.64 128	29.57 258	81.31 263	64.10 125	70.18 167	81.56 233

APD-MVS_3200maxsize		69.62 150	68.23 145	73.80 169	81.58 147	48.22 230	81.91 206	79.50 208	48.21 275	64.24 137	89.75 90	31.91 244	87.55 175	63.08 130	73.85 135	85.64 171

v10		66.61 206	64.20 215	73.83 168	72.59 274	53.37 107	81.88 207	79.91 200	61.11 125	54.09 255	75.60 262	40.06 156	88.26 155	56.47 189	56.10 272	79.86 263

EI-MVSNet-UG-set		72.37 105	71.73 99	74.29 156	81.60 145	49.29 204	81.85 208	88.64 36	65.29 66	65.05 123	88.29 115	43.18 122	91.83 57	63.74 127	67.97 180	81.75 230

ppachtmachnet_test		58.56 265	54.34 274	71.24 221	71.42 287	54.74 71	81.84 209	72.27 296	49.02 272	45.86 299	68.99 307	26.27 279	83.30 252	30.12 309	43.23 322	75.69 297

test_0402		56.45 277	53.03 281	66.69 272	76.78 228	50.31 184	81.76 210	69.61 312	42.79 312	43.88 301	72.13 292	22.82 300	86.46 201	16.57 342	50.94 300	63.31 336

旧先验2							81.73 211		45.53 295	74.66 35			70.48 328	58.31 171

thres100view900		66.87 203	65.42 202	71.24 221	83.29 104	43.15 285	81.67 212	87.78 52	59.04 166	55.92 242	82.18 194	43.73 113	87.80 166	28.80 313	66.36 190	82.78 222

MVSFormer		73.53 90	72.19 93	77.57 85	83.02 113	55.24 54	81.63 213	81.44 178	50.28 264	76.67 26	90.91 60	44.82 101	86.11 208	60.83 146	80.09 84	91.36 54

test_djsdf		63.84 226	61.56 230	70.70 230	68.78 303	44.69 272	81.63 213	81.44 178	50.28 264	52.27 267	76.26 254	26.72 277	86.11 208	60.83 146	55.84 277	81.29 245

新几何2							81.61 215

TSAR-MVS + MP.		78.31 25	78.26 20	78.48 60	81.33 157	56.31 37	81.59 216	86.41 75	69.61 21	81.72 11	88.16 118	55.09 19	88.04 161	74.12 60	86.31 30	91.09 60
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
SR-MVS-dyc-post		68.27 173	66.87 168	72.48 195	80.96 161	48.14 233	81.54 217	76.98 256	46.42 289	62.75 155	89.42 95	31.17 249	86.09 212	60.52 152	72.06 153	83.19 213

RE-MVS-def			66.66 175		80.96 161	48.14 233	81.54 217	76.98 256	46.42 289	62.75 155	89.42 95	29.28 261		60.52 152	72.06 153	83.19 213

V42		67.66 182	65.60 197	73.86 166	70.69 294	53.63 95	81.50 219	78.61 228	63.85 80	59.49 185	77.49 234	37.98 170	87.65 172	62.33 134	58.43 247	80.29 257

DU-MVS		66.84 204	65.74 193	70.16 238	73.27 266	42.59 290	81.50 219	82.92 158	63.53 88	58.51 202	82.11 195	40.75 146	84.64 241	53.11 207	55.96 274	83.24 211

HyFIR lowres test		69.94 142	67.58 156	77.04 98	77.11 226	57.29 20	81.49 221	79.11 218	58.27 179	58.86 197	80.41 210	42.33 128	86.96 188	61.91 138	68.68 176	86.87 146

IterMVS-LS		66.63 205	65.36 203	70.42 234	75.10 247	48.90 212	81.45 222	76.69 263	61.05 127	55.71 243	77.10 241	45.86 87	83.65 248	57.44 183	57.88 258	78.70 267
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
jajsoiax		63.21 232	60.84 235	70.32 236	68.33 308	44.45 274	81.23 223	81.05 185	53.37 245	50.96 276	77.81 231	17.49 324	85.49 225	59.31 160	58.05 253	81.02 248

HPM-MVS_fast		67.86 178	66.28 180	72.61 190	80.67 170	48.34 228	81.18 224	75.95 270	50.81 263	59.55 184	88.05 121	27.86 269	85.98 216	58.83 163	73.58 136	83.51 205

tfpnnormal		61.47 246	59.09 249	68.62 259	76.29 233	41.69 296	81.14 225	85.16 106	54.48 238	51.32 272	73.63 277	32.32 237	86.89 190	21.78 334	55.71 278	77.29 286

IS-MVSNet		68.80 162	67.55 159	72.54 192	78.50 203	43.43 283	81.03 226	79.35 213	59.12 165	57.27 228	86.71 139	46.05 82	87.70 171	44.32 260	75.60 121	86.49 155

eth_miper_zixun_eth		66.98 201	65.28 204	72.06 202	75.61 242	50.40 178	81.00 227	76.97 259	62.00 107	56.99 230	76.97 242	44.84 100	85.58 222	58.75 165	54.42 286	80.21 258

mvs_tets		62.96 234	60.55 237	70.19 237	68.22 311	44.24 278	80.90 228	80.74 189	52.99 248	50.82 278	77.56 232	16.74 327	85.44 226	59.04 162	57.94 255	80.89 249

tttt0517		68.33 171	66.29 179	74.46 150	78.08 208	49.06 206	80.88 229	89.08 23	54.40 239	54.75 248	80.77 208	51.31 37	90.33 93	49.35 233	58.01 254	83.99 194

FC-MVSNet-test		67.49 186	67.91 148	66.21 275	76.06 235	33.06 327	80.82 230	87.18 61	64.44 73	54.81 246	82.87 179	50.40 46	82.60 255	48.05 241	66.55 188	82.98 218

sss		70.49 131	70.13 121	71.58 217	81.59 146	39.02 308	80.78 231	84.71 119	59.34 154	66.61 103	88.09 119	37.17 189	85.52 223	61.82 140	71.02 160	90.20 83

HPM-MVS		72.60 101	71.50 102	75.89 125	82.02 135	51.42 160	80.70 232	83.05 155	56.12 220	64.03 139	89.53 93	37.55 180	88.37 146	70.48 82	80.04 86	87.88 130
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
pmmvs6		59.64 253	57.15 259	67.09 268	66.01 315	36.86 317	80.50 233	78.64 226	45.05 299	49.05 283	73.94 272	27.28 273	86.10 210	43.96 262	49.94 302	78.31 275

IterMVS		63.77 228	61.67 228	70.08 240	72.68 273	51.24 165	80.44 234	75.51 272	60.51 135	51.41 271	73.70 276	32.08 240	78.91 285	54.30 202	54.35 287	80.08 260
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
IterMVS-SCA-FT		59.12 258	58.81 252	60.08 307	70.68 295	45.07 268	80.42 235	74.25 282	43.54 309	50.02 279	73.73 273	31.97 241	56.74 339	51.06 224	53.60 292	78.42 273

ACMH+	54.58 15	58.55 266	55.24 269	68.50 261	74.68 253	45.80 262	80.27 236	70.21 310	47.15 281	42.77 308	75.48 264	16.73 328	85.98 216	35.10 293	54.78 284	73.72 310

Anonymous20231206		59.08 260	57.59 256	63.55 290	68.77 304	32.14 332	80.26 237	79.78 202	50.00 267	49.39 281	72.39 289	26.64 278	78.36 288	33.12 300	57.94 255	80.14 259

1314		71.11 121	69.41 129	76.22 116	79.32 184	50.49 175	80.23 238	85.14 108	59.44 150	58.93 194	88.89 106	33.83 225	89.60 113	61.49 141	77.42 104	88.57 119

1121		68.79 163	66.77 171	74.82 144	83.08 110	53.46 100	80.23 238	71.53 303	45.47 296	66.31 108	87.19 132	34.02 220	85.13 233	52.78 213	80.36 81	85.87 167

MVS		76.91 44	75.48 54	81.23 16	84.56 76	55.21 56	80.23 238	91.64 2	58.65 174	65.37 119	91.48 49	45.72 89	95.05 14	72.11 75	89.52 8	93.44 7

ACMH	53.70 16	59.78 252	55.94 267	71.28 220	76.59 229	48.35 227	80.15 241	76.11 268	49.74 268	41.91 311	73.45 280	16.50 329	90.31 94	31.42 305	57.63 260	75.17 301
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
pmmvs4		63.34 231	61.07 234	70.16 238	70.14 296	50.53 174	79.97 242	71.41 305	55.08 231	54.12 254	78.58 224	32.79 233	82.09 259	50.33 226	57.22 262	77.86 280

abl_6		68.03 175	66.15 183	73.66 172	78.54 201	48.48 224	79.77 243	78.04 237	47.39 279	63.70 145	88.25 116	28.21 264	89.06 119	60.17 158	71.25 158	83.45 206

MVS_111021_LR		69.07 154	67.91 148	72.54 192	77.27 220	49.56 198	79.77 243	73.96 286	59.33 156	60.73 171	87.82 123	30.19 256	81.53 261	69.94 84	72.19 152	86.53 154

CNLPA		60.59 250	58.44 253	67.05 270	79.21 186	47.26 244	79.75 245	64.34 323	42.46 314	51.90 270	83.94 164	27.79 271	75.41 308	37.12 280	59.49 238	78.47 271

EI-MVSNet		69.70 147	68.70 138	72.68 189	75.00 249	48.90 212	79.54 246	87.16 62	61.05 127	63.88 143	83.74 168	45.87 86	90.44 89	57.42 184	64.68 201	78.70 267

CVMVSNet		60.85 249	60.44 239	62.07 296	75.00 249	32.73 329	79.54 246	73.49 291	36.98 326	56.28 240	83.74 168	29.28 261	69.53 330	46.48 250	63.23 214	83.94 199

PVSNet_0	57.04 13	61.19 247	57.24 258	73.02 183	77.45 218	50.31 184	79.43 248	77.36 251	63.96 79	47.51 291	72.45 288	25.03 289	83.78 247	52.76 216	19.22 345	84.96 181

PCF-MVS	61.03 10	70.10 136	68.40 142	75.22 139	77.15 225	51.99 144	79.30 249	82.12 166	56.47 217	61.88 163	86.48 144	43.98 108	87.24 181	55.37 195	72.79 146	86.43 157
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
PLC	52.38 18	60.89 248	58.97 251	66.68 273	81.77 140	45.70 263	78.96 250	74.04 285	43.66 308	47.63 288	83.19 178	23.52 297	77.78 298	37.47 277	60.46 232	76.55 293
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
D2MVS		63.49 230	61.39 232	69.77 244	69.29 301	48.93 211	78.89 251	77.71 244	60.64 134	49.70 280	72.10 294	27.08 275	83.48 250	54.48 201	62.65 221	76.90 288

OMC-MVS		65.97 216	65.06 207	68.71 257	72.97 269	42.58 292	78.61 252	75.35 275	54.72 234	59.31 188	86.25 145	33.30 228	77.88 295	57.99 174	67.05 185	85.66 170

PAPM_NR		71.80 114	69.98 123	77.26 96	81.54 149	53.34 110	78.60 253	85.25 103	53.46 243	60.53 174	88.66 109	45.69 90	89.24 117	56.49 188	79.62 91	89.19 103

mvs_anonymous		72.29 108	70.74 111	76.94 104	82.85 120	54.72 73	78.43 254	81.54 176	63.77 81	61.69 164	79.32 217	51.11 38	85.31 227	62.15 137	75.79 118	90.79 68

test222					79.36 182	50.97 167	77.99 255	67.84 315	42.54 313	62.84 154	86.53 142	30.26 255			76.91 109	85.23 177

v7n		62.50 239	59.27 248	72.20 199	67.25 314	49.83 193	77.87 256	80.12 195	52.50 251	48.80 284	73.07 281	32.10 239	87.90 163	46.83 249	54.92 282	78.86 266

test20.03		55.22 284	54.07 277	58.68 311	63.14 329	25.00 343	77.69 257	74.78 278	52.64 249	43.43 304	72.39 289	26.21 280	74.76 310	29.31 311	47.05 312	76.28 295

testdata1							77.55 258		64.14 76

PEN-MVS		58.35 268	57.15 259	61.94 298	67.55 313	34.39 321	77.01 259	78.35 233	51.87 256	47.72 287	76.73 248	33.91 222	73.75 316	34.03 296	47.17 310	77.68 282

WR-MVS_H		58.91 261	58.04 254	61.54 301	69.07 302	33.83 324	76.91 260	81.99 167	51.40 260	48.17 285	74.67 268	40.23 152	74.15 312	31.78 304	48.10 304	76.64 291

CP-MVSNet		58.54 267	57.57 257	61.46 302	68.50 306	33.96 323	76.90 261	78.60 229	51.67 259	47.83 286	76.60 250	34.99 213	72.79 321	35.45 289	47.58 306	77.64 284

PS-CasMVS		58.12 269	57.03 261	61.37 303	68.24 310	33.80 325	76.73 262	78.01 238	51.20 261	47.54 290	76.20 258	32.85 231	72.76 322	35.17 291	47.37 308	77.55 285

tpm		68.36 169	67.48 161	70.97 227	79.93 178	51.34 162	76.58 263	78.75 224	67.73 35	63.54 149	74.86 267	48.33 57	72.36 324	53.93 204	63.71 207	89.21 102

MVS_0304		56.72 273	55.17 270	61.37 303	70.71 292	36.80 318	75.74 264	68.75 314	44.11 306	52.53 264	68.20 309	15.05 333	74.53 311	42.98 266	58.44 246	72.79 317

DTE-MVSNet		57.03 272	55.73 268	60.95 306	65.94 316	32.57 330	75.71 265	77.09 255	51.16 262	46.65 295	76.34 253	32.84 232	73.22 320	30.94 308	44.87 318	77.06 287

tpmrst		71.04 122	69.77 125	74.86 143	83.19 106	55.86 43	75.64 266	78.73 225	67.88 33	64.99 126	73.73 273	49.96 49	79.56 283	65.92 109	67.85 182	89.14 105

CostFormer		73.89 85	72.30 90	78.66 56	82.36 133	56.58 28	75.56 267	85.30 99	66.06 54	70.50 80	76.88 246	57.02 12	89.06 119	68.27 94	68.74 175	90.33 79

HY-MVS	67.03 5	73.90 84	73.14 79	76.18 118	84.70 75	47.36 242	75.56 267	86.36 77	66.27 48	70.66 78	83.91 165	51.05 39	89.31 116	67.10 100	72.61 148	91.88 39

K. test v3		54.04 289	49.42 297	67.92 263	68.55 305	42.57 293	75.51 269	63.07 325	52.07 253	39.21 320	64.59 320	19.34 317	82.21 256	37.11 281	25.31 342	78.97 265

Vis-MVSNet (Re-imp)		65.52 218	65.63 195	65.17 283	77.49 217	30.54 334	75.49 270	77.73 243	59.34 154	52.26 268	86.69 140	49.38 53	80.53 271	37.07 282	75.28 123	84.42 187

pmmvs-eth3d		55.97 281	52.78 285	65.54 279	61.02 335	46.44 253	75.36 271	67.72 316	49.61 269	43.65 303	67.58 311	21.63 308	77.04 300	44.11 261	44.33 319	73.15 316

TAPA-MVS	56.12 14	61.82 245	60.18 241	66.71 271	78.48 204	37.97 313	75.19 272	76.41 267	46.82 284	57.04 229	86.52 143	27.67 272	77.03 301	26.50 325	67.02 186	85.14 178
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
FMVSNet5		58.61 264	56.45 263	65.10 284	77.20 224	39.74 306	74.77 273	77.12 254	50.27 266	43.28 306	67.71 310	26.15 281	76.90 303	36.78 285	54.78 284	78.65 269

SixPastTwentyTwo		54.37 286	50.10 293	67.21 267	70.70 293	41.46 299	74.73 274	64.69 321	47.56 278	39.12 321	69.49 303	18.49 321	84.69 240	31.87 303	34.20 337	75.48 299

F-COLMAP		55.96 282	53.65 280	62.87 294	72.76 272	42.77 289	74.70 275	70.37 309	40.03 317	41.11 315	79.36 216	17.77 323	73.70 317	32.80 301	53.96 289	72.15 318

MSDG		59.44 254	55.14 272	72.32 198	74.69 252	50.71 169	74.39 276	73.58 289	44.44 303	43.40 305	77.52 233	19.45 316	90.87 79	31.31 306	57.49 261	75.38 300

tpm2		70.82 126	68.44 141	77.98 76	80.78 166	56.11 39	74.21 277	81.28 183	60.24 138	68.04 92	75.27 265	52.26 32	88.50 143	55.82 194	68.03 179	89.33 97

UniMVSNet_ETH3D		62.51 238	60.49 238	68.57 260	68.30 309	40.88 304	73.89 278	79.93 199	51.81 258	54.77 247	79.61 214	24.80 290	81.10 264	49.93 228	61.35 228	83.73 202

UA-Net		67.32 192	66.23 181	70.59 231	78.85 192	41.23 301	73.60 279	75.45 274	61.54 118	66.61 103	84.53 159	38.73 166	86.57 200	42.48 270	74.24 131	83.98 196

ab-mvs		70.65 129	69.11 135	75.29 136	80.87 165	46.23 259	73.48 280	85.24 104	59.99 141	66.65 101	80.94 206	43.13 125	88.69 134	63.58 128	68.07 178	90.95 66

LS3D		56.40 278	53.82 278	64.12 287	81.12 159	45.69 264	73.42 281	66.14 318	35.30 333	43.24 307	79.88 212	22.18 305	79.62 282	19.10 340	64.00 205	67.05 329

testmvs		6.14 326	8.18 329	0.01 338	0.01 359	0.00 361	73.40 282	0.00 360	0.00 355	0.02 356	0.15 356	0.00 361	0.00 356	0.02 354	0.00 354	0.02 352

UnsupCasMVSNet_eth		57.56 270	55.15 271	64.79 286	64.57 324	33.12 326	73.17 283	83.87 140	58.98 169	41.75 312	70.03 302	22.54 301	79.92 278	46.12 254	35.31 332	81.32 244

testing_2		63.60 229	59.86 243	74.82 144	61.87 333	52.39 137	73.06 284	82.76 160	61.49 120	39.96 318	67.39 312	21.06 311	88.34 148	67.07 101	64.10 203	83.72 203

anonymousdsp		60.46 251	57.65 255	68.88 251	63.63 327	45.09 267	72.93 285	78.63 227	46.52 287	51.12 273	72.80 285	21.46 309	83.07 254	57.79 179	53.97 288	78.47 271

EU-MVSNet		52.63 295	50.72 291	58.37 312	62.69 331	28.13 341	72.60 286	75.97 269	30.94 335	40.76 317	72.11 293	20.16 314	70.80 326	35.11 292	46.11 315	76.19 296

dp		64.41 222	61.58 229	72.90 185	82.40 131	54.09 87	72.53 287	76.59 265	60.39 136	55.68 244	70.39 301	35.18 211	76.90 303	39.34 274	61.71 227	87.73 134

N_pmnet		41.25 308	39.77 311	45.66 324	68.50 306	0.82 359	72.51 288	0.38 359	35.61 330	35.26 329	61.51 325	20.07 315	67.74 331	23.51 331	40.63 325	68.42 328

MDTV_nov1_ep13			61.56 230		81.68 141	55.12 59	72.41 289	78.18 235	59.19 159	58.85 198	69.29 305	34.69 214	86.16 207	36.76 286	62.96 219

YYNet1		53.82 291	49.96 294	65.41 281	70.09 298	48.95 209	72.30 290	71.66 301	44.25 304	31.89 337	63.07 323	23.73 295	73.95 314	33.26 298	39.40 328	73.34 313

MDA-MVSNet_test_wron		53.82 291	49.95 295	65.43 280	70.13 297	49.05 207	72.30 290	71.65 302	44.23 305	31.85 338	63.13 322	23.68 296	74.01 313	33.25 299	39.35 329	73.23 315

testgi		54.25 288	52.57 287	59.29 309	62.76 330	21.65 347	72.21 292	70.47 308	53.25 246	41.94 310	77.33 237	14.28 334	77.95 294	29.18 312	51.72 298	78.28 276

PatchmatchNet		67.07 199	63.63 219	77.40 89	83.10 107	58.03 10	72.11 293	77.77 242	58.85 171	59.37 186	70.83 297	37.84 172	84.93 237	42.96 267	69.83 169	89.26 99
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
test123		6.01 327	8.01 330	0.01 338	0.00 360	0.01 360	71.93 294	0.00 360	0.00 355	0.02 356	0.11 357	0.00 361	0.00 356	0.02 354	0.00 354	0.02 352

EPMVS		68.45 168	65.44 201	77.47 88	84.91 72	56.17 38	71.89 295	81.91 171	61.72 114	60.85 169	72.49 286	36.21 200	87.06 185	47.32 245	71.62 155	89.17 104

UnsupCasMVSNet_bld		53.86 290	50.53 292	63.84 288	63.52 328	34.75 320	71.38 296	81.92 170	46.53 286	38.95 322	57.93 333	20.55 313	80.20 276	39.91 273	34.09 338	76.57 292

COLMAP_ROB	43.60 20	50.90 299	48.05 300	59.47 308	67.81 312	40.57 305	71.25 297	62.72 327	36.49 328	36.19 326	73.51 278	13.48 335	73.92 315	20.71 336	50.26 301	63.92 335
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
MDTV_nov1_ep13_2view						43.62 282	71.13 298		54.95 232	59.29 189		36.76 195		46.33 252		87.32 141

test_post1							70.84 299				14.72 353	34.33 217	83.86 244	48.80 235

new-patchmatchnet		48.21 302	46.55 304	53.18 318	57.73 339	18.19 351	70.24 300	71.02 307	45.70 293	33.70 332	60.23 328	18.00 322	69.86 329	27.97 319	34.35 335	71.49 323

pmmvs3		45.53 307	41.55 310	57.44 314	48.97 345	39.68 307	70.06 301	57.66 331	28.32 338	34.06 331	57.29 334	8.50 343	66.85 332	34.86 294	34.26 336	65.80 332

tpmvs		62.45 241	59.42 246	71.53 218	83.93 89	54.32 81	70.03 302	77.61 245	51.91 255	53.48 260	68.29 308	37.91 171	86.66 195	33.36 297	58.27 248	73.62 311

tpm cat1		66.28 211	62.78 221	76.77 109	81.40 155	57.14 21	70.03 302	77.19 252	53.00 247	58.76 200	70.73 300	46.17 79	86.73 193	43.27 264	64.46 202	86.44 156

PatchMatch-RL		56.66 274	53.75 279	65.37 282	77.91 213	45.28 266	69.78 304	60.38 328	41.35 315	47.57 289	73.73 273	16.83 326	76.91 302	36.99 283	59.21 240	73.92 309

MDA-MVSNet-bldmvs		51.56 298	47.75 303	63.00 293	71.60 285	47.32 243	69.70 305	72.12 297	43.81 307	27.65 342	63.38 321	21.97 307	75.96 305	27.30 322	32.19 339	65.70 333

miper_lstm_enhance		63.91 225	62.30 224	68.75 256	75.06 248	46.78 248	69.02 306	81.14 184	59.68 146	52.76 263	72.39 289	40.71 148	77.99 293	56.81 187	53.09 294	81.48 236

GG-mvs-BLEND				77.77 80	86.68 42	50.61 171	68.67 307	88.45 44		68.73 86	87.45 130	59.15 8	90.67 82	54.83 197	87.67 15	92.03 35

OurMVSNet-221017-0		52.39 296	48.73 298	63.35 292	65.21 320	38.42 311	68.54 308	64.95 320	38.19 321	39.57 319	71.43 296	13.23 336	79.92 278	37.16 279	40.32 327	71.72 320

MIMVSNet1		50.35 300	47.81 301	57.96 313	61.53 334	27.80 342	67.40 309	74.06 284	43.25 310	33.31 336	65.38 319	16.03 330	71.34 325	21.80 333	47.55 307	74.75 303

MTAPA		72.73 99	71.22 107	77.27 94	81.54 149	53.57 96	67.06 310	81.31 180	59.41 151	68.39 89	90.96 58	36.07 202	89.01 123	73.80 63	82.45 61	89.23 100

MIMVSNet		63.12 233	60.29 240	71.61 214	75.92 239	46.65 250	65.15 311	81.94 168	59.14 163	54.65 249	69.47 304	25.74 283	80.63 269	41.03 271	69.56 172	87.55 136

XVG-OURS-SEG-HR		62.02 243	59.54 245	69.46 247	65.30 319	45.88 261	65.06 312	73.57 290	46.45 288	57.42 226	83.35 175	26.95 276	78.09 291	53.77 205	64.03 204	84.42 187

XVG-OURS		61.88 244	59.34 247	69.49 246	65.37 318	46.27 257	64.80 313	73.49 291	47.04 282	57.41 227	82.85 180	25.15 288	78.18 289	53.00 210	64.98 196	84.01 193

gg-mvs-nofinetune		67.43 188	64.53 212	76.13 119	85.95 46	47.79 239	64.38 314	88.28 45	39.34 318	66.62 102	41.27 339	58.69 11	89.00 125	49.64 231	86.62 27	91.59 44

XVG-ACMP-BASELINE		56.03 280	52.85 284	65.58 278	61.91 332	40.95 303	63.36 315	72.43 295	45.20 298	46.02 297	74.09 270	9.20 342	78.12 290	45.13 256	58.27 248	77.66 283

TinyColmap		48.15 303	44.49 307	59.13 310	65.73 317	38.04 312	63.34 316	62.86 326	38.78 319	29.48 340	67.23 314	6.46 347	73.30 319	24.59 328	41.90 324	66.04 331

MVS-HIRNet		49.01 301	44.71 305	61.92 299	76.06 235	46.61 251	63.23 317	54.90 334	24.77 340	33.56 333	36.60 342	21.28 310	75.88 306	29.49 310	62.54 222	63.26 337

PM-MVS		46.92 305	43.76 309	56.41 315	52.18 342	32.26 331	63.21 318	38.18 346	37.99 323	40.78 316	66.20 315	5.09 350	65.42 333	48.19 240	41.99 323	71.54 322

AllTest		47.32 304	44.66 306	55.32 316	65.08 321	37.50 315	62.96 319	54.25 336	35.45 331	33.42 334	72.82 283	9.98 339	59.33 336	24.13 329	43.84 320	69.13 325

USDC		54.36 287	51.23 290	63.76 289	64.29 325	37.71 314	62.84 320	73.48 293	56.85 206	35.47 328	71.94 295	9.23 341	78.43 287	38.43 276	48.57 303	75.13 302

Patchmatch-RL test		58.72 263	54.32 275	71.92 211	63.91 326	44.25 277	61.73 321	55.19 333	57.38 200	49.31 282	54.24 336	37.60 179	80.89 266	62.19 136	47.28 309	90.63 71

SCA		63.84 226	60.01 242	75.32 135	78.58 200	57.92 11	61.61 322	77.53 246	56.71 211	57.75 217	70.77 298	31.97 241	79.91 280	48.80 235	56.36 266	88.13 126

CMPMVS	40.41 21	55.34 283	52.64 286	63.46 291	60.88 336	43.84 280	61.58 323	71.06 306	30.43 336	36.33 325	74.63 269	24.14 293	75.44 307	48.05 241	66.62 187	71.12 324
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
LCM-MVSNet-Re		58.82 262	56.54 262	65.68 277	79.31 185	29.09 339	61.39 324	45.79 341	60.73 132	37.65 324	72.47 287	31.42 247	81.08 265	49.66 230	70.41 164	86.87 146

CR-MVSNet		62.47 240	59.04 250	72.77 188	73.97 261	56.57 29	60.52 325	71.72 299	60.04 139	57.49 223	65.86 316	38.94 163	80.31 273	42.86 268	59.93 234	81.42 237

RPMNet		59.29 255	54.25 276	74.42 152	73.97 261	56.57 29	60.52 325	76.98 256	35.72 329	57.49 223	58.87 332	37.73 176	85.26 229	27.01 323	59.93 234	81.42 237

Patchmtry		56.56 276	52.95 283	67.42 266	72.53 276	50.59 173	59.05 327	71.72 299	37.86 324	46.92 292	65.86 316	38.94 163	80.06 277	36.94 284	46.72 314	71.60 321

TDRefinement		40.91 309	38.37 312	48.55 322	50.45 343	33.03 328	58.98 328	50.97 339	28.50 337	29.89 339	67.39 312	6.21 349	54.51 340	17.67 341	35.25 333	58.11 338

PatchT		56.60 275	52.97 282	67.48 265	72.94 270	46.16 260	57.30 329	73.78 287	38.77 320	54.37 252	57.26 335	37.52 181	78.06 292	32.02 302	52.79 295	78.23 278

ANet_high		34.39 312	29.59 317	48.78 321	30.34 353	22.28 345	55.53 330	63.79 324	38.11 322	15.47 346	36.56 343	6.94 344	59.98 335	13.93 344	5.64 353	64.08 334

ADS-MVSNet2		55.21 285	51.44 289	66.51 274	80.60 171	49.56 198	55.03 331	65.44 319	44.72 300	51.00 274	61.19 326	22.83 298	75.41 308	28.54 316	53.63 290	74.57 305

ADS-MVSNet		56.17 279	51.95 288	68.84 252	80.60 171	53.07 120	55.03 331	70.02 311	44.72 300	51.00 274	61.19 326	22.83 298	78.88 286	28.54 316	53.63 290	74.57 305

RPSCF		45.77 306	44.13 308	50.68 320	57.67 340	29.66 335	54.92 333	45.25 343	26.69 339	45.92 298	75.92 261	17.43 325	45.70 347	27.44 321	45.95 316	76.67 290

new_pmnet		33.56 313	31.89 316	38.59 327	49.01 344	20.42 348	51.01 334	37.92 347	20.58 341	23.45 343	46.79 338	6.66 346	49.28 345	20.00 339	31.57 341	46.09 343

E-PMN		19.16 319	18.40 323	21.44 333	36.19 351	13.63 354	47.59 335	30.89 351	10.73 349	5.91 353	16.59 349	3.66 353	39.77 349	5.95 350	8.14 348	10.92 348

EMVS		18.42 320	17.66 324	20.71 334	34.13 352	12.64 355	46.94 336	29.94 352	10.46 351	5.58 354	14.93 352	4.23 352	38.83 350	5.24 352	7.51 350	10.67 349

CHOSEN 280x420		57.53 271	56.38 264	60.97 305	74.01 259	48.10 235	46.30 337	54.31 335	48.18 276	50.88 277	77.43 236	38.37 169	59.16 338	54.83 197	63.14 217	75.66 298

LTVRE_ROB	45.45 19	52.73 294	49.74 296	61.69 300	69.78 299	34.99 319	44.52 338	67.60 317	43.11 311	43.79 302	74.03 271	18.54 320	81.45 262	28.39 318	57.94 255	68.62 327
Andreas Kuhn, Heiko HirschmÃ¼ller, Daniel Scharstein, Helmut Mayer: A TV Prior for High-Quality Scalable Multi-View Stereo Reconstruction. International Journal of Computer Vision 2016
LF4IMVS		33.04 314	32.55 315	34.52 330	40.96 348	22.03 346	44.45 339	35.62 349	20.42 342	28.12 341	62.35 324	5.03 351	31.88 352	21.61 335	34.42 334	49.63 341

Patchmatch-test		53.33 293	48.17 299	68.81 254	73.31 264	42.38 294	42.98 340	58.23 330	32.53 334	38.79 323	70.77 298	39.66 159	73.51 318	25.18 327	52.06 297	90.55 72

PMMVS2		26.71 317	22.98 321	37.87 328	36.89 350	8.51 357	42.51 341	29.32 353	19.09 345	13.01 347	37.54 340	2.23 354	53.11 341	14.54 343	11.71 346	51.99 340

FPMVS		35.40 311	33.67 314	40.57 326	46.34 347	28.74 340	41.05 342	57.05 332	20.37 343	22.27 344	53.38 337	6.87 345	44.94 348	8.62 346	47.11 311	48.01 342

PMVS	19.57 22	25.07 318	22.43 322	32.99 331	23.12 356	22.98 344	40.98 343	35.19 350	15.99 346	11.95 349	35.87 344	1.47 358	49.29 344	5.41 351	31.90 340	26.70 345
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
JIA-IIPM		52.33 297	47.77 302	66.03 276	71.20 290	46.92 247	40.00 344	76.48 266	37.10 325	46.73 293	37.02 341	32.96 230	77.88 295	35.97 287	52.45 296	73.29 314

DSMNet-mixed		38.35 310	35.36 313	47.33 323	48.11 346	14.91 353	37.87 345	36.60 348	19.18 344	34.37 330	59.56 331	15.53 331	53.01 342	20.14 338	46.89 313	74.07 307

ambc				62.06 297	53.98 341	29.38 337	35.08 346	79.65 206		41.37 313	59.96 329	6.27 348	82.15 257	35.34 290	38.22 330	74.65 304

LCM-MVSNet		28.07 315	23.85 320	40.71 325	27.46 355	18.93 350	30.82 347	46.19 340	12.76 348	16.40 345	34.70 345	1.90 356	48.69 346	20.25 337	24.22 343	54.51 339

wuyk23d		9.11 324	8.77 328	10.15 336	40.18 349	16.76 352	20.28 348	1.01 358	2.58 353	2.66 355	0.98 355	0.23 360	12.49 354	4.08 353	6.90 351	1.19 351

MVE	16.60 23	17.34 322	13.39 325	29.16 332	28.43 354	19.72 349	13.73 349	23.63 354	7.23 352	7.96 351	21.41 347	0.80 359	36.08 351	6.97 348	10.39 347	31.69 344
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
Gipuma		27.47 316	24.26 319	37.12 329	60.55 337	29.17 338	11.68 350	60.00 329	14.18 347	10.52 350	15.12 351	2.20 355	63.01 334	8.39 347	35.65 331	19.18 346
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
tmp_tt		9.44 323	10.68 326	5.73 337	2.49 358	4.21 358	10.48 351	18.04 355	0.34 354	12.59 348	20.49 348	11.39 337	7.03 355	13.84 345	6.46 352	5.95 350

uanet_test		0.00 329	0.00 332	0.00 340	0.00 360	0.00 361	0.00 352	0.00 360	0.00 355	0.00 358	0.00 358	0.00 361	0.00 356	0.00 356	0.00 354	0.00 354

cdsmvs_eth3d_5k		18.33 321	24.44 318	0.00 340	0.00 360	0.00 361	0.00 352	89.40 17	0.00 355	0.00 358	92.02 36	38.55 167	0.00 356	0.00 356	0.00 354	0.00 354

pcd_1.5k_mvsjas		3.15 328	4.20 331	0.00 340	0.00 360	0.00 361	0.00 352	0.00 360	0.00 355	0.00 358	0.00 358	37.77 173	0.00 356	0.00 356	0.00 354	0.00 354

sosnet-low-res		0.00 329	0.00 332	0.00 340	0.00 360	0.00 361	0.00 352	0.00 360	0.00 355	0.00 358	0.00 358	0.00 361	0.00 356	0.00 356	0.00 354	0.00 354

sosnet		0.00 329	0.00 332	0.00 340	0.00 360	0.00 361	0.00 352	0.00 360	0.00 355	0.00 358	0.00 358	0.00 361	0.00 356	0.00 356	0.00 354	0.00 354

uncertanet		0.00 329	0.00 332	0.00 340	0.00 360	0.00 361	0.00 352	0.00 360	0.00 355	0.00 358	0.00 358	0.00 361	0.00 356	0.00 356	0.00 354	0.00 354

Regformer		0.00 329	0.00 332	0.00 340	0.00 360	0.00 361	0.00 352	0.00 360	0.00 355	0.00 358	0.00 358	0.00 361	0.00 356	0.00 356	0.00 354	0.00 354

ab-mvs-re		7.68 325	10.24 327	0.00 340	0.00 360	0.00 361	0.00 352	0.00 360	0.00 355	0.00 358	92.12 33	0.00 361	0.00 356	0.00 356	0.00 354	0.00 354

uanet		0.00 329	0.00 332	0.00 340	0.00 360	0.00 361	0.00 352	0.00 360	0.00 355	0.00 358	0.00 358	0.00 361	0.00 356	0.00 356	0.00 354	0.00 354

ZD-MVS					89.55 11	53.46 100		84.38 124	57.02 204	73.97 43	91.03 54	44.57 104	91.17 71	75.41 52	81.78 69

IU-MVS					89.48 14	57.49 16		91.38 5	66.22 49	88.26 1				82.83 7	87.60 16	92.44 25

test_241102_TWO								88.76 34	57.50 198	83.60 5	94.09 4	56.14 16	96.37 5	82.28 11	87.43 18	92.55 23

test_241102_ONE					89.48 14	56.89 25		88.94 27	57.53 196	84.61 3	93.29 13	58.81 9	96.45 1

test_0728_THIRD									58.00 184	81.91 9	93.64 10	56.54 13	96.44 2	81.64 16	86.86 23	92.23 29

GSMVS																88.13 126

test_part2					89.33 19	55.48 47				82.27 8

sam_mvs1												38.86 165				88.13 126

sam_mvs												35.99 206

MTGPA								81.31 180

test_post											16.22 350	37.52 181	84.72 239

patchmatchnet-post											59.74 330	38.41 168	79.91 280

gm-plane-assit					83.24 105	54.21 84			70.91 12		88.23 117		95.25 12	66.37 105

test9_res														78.72 27	85.44 40	91.39 52

agg_prior2														75.65 47	85.11 44	91.01 63

agg_prior					85.64 53	54.92 66		83.61 145		72.53 60			88.10 159

TestCases				55.32 316	65.08 321	37.50 315		54.25 336	35.45 331	33.42 334	72.82 283	9.98 339	59.33 336	24.13 329	43.84 320	69.13 325

test_prior				78.39 65	86.35 44	54.91 68		85.45 91					89.70 109			90.55 72

新几何1				73.30 181	83.10 107	53.48 99		71.43 304	45.55 294	66.14 109	87.17 133	33.88 224	80.54 270	48.50 238	80.33 82	85.88 166

旧先验1					81.57 148	47.48 241		71.83 298			88.66 109	36.94 192			78.34 99	88.67 115

原ACMM1				76.13 119	84.89 73	54.59 78		85.26 102	51.98 254	66.70 100	87.07 135	40.15 154	89.70 109	51.23 222	85.06 45	84.10 190

testdata2													77.81 297	45.64 255

segment_acmp												44.97 98

testdata				67.08 269	77.59 215	45.46 265		69.20 313	44.47 302	71.50 72	88.34 113	31.21 248	70.76 327	52.20 219	75.88 117	85.03 179

test12				79.24 36	86.89 40	56.08 40		85.16 106		72.27 65		47.15 70	91.10 72		85.93 33	90.54 75

plane_prior7					77.95 210	48.46 226

plane_prior6					78.42 205	49.39 202						36.04 204

plane_prior5								82.59 161					88.30 152	65.46 115	72.34 150	84.49 185

plane_prior4											83.28 176

plane_prior3						48.95 209			64.01 77	62.15 159

plane_prior1					78.31 207

n2								0.00 360

nn								0.00 360

door-mid								41.31 345

lessismore_v0				67.98 262	64.76 323	41.25 300		45.75 342		36.03 327	65.63 318	19.29 318	84.11 243	35.67 288	21.24 344	78.59 270

LGP-MVS_train				72.02 203	74.42 255	48.60 218		80.64 190	54.69 235	53.75 257	83.83 166	25.73 284	86.98 186	60.33 156	64.71 198	80.48 254

test11								84.25 130

door								43.27 344

HQP5-MVS						51.56 155

BP-MVS														66.70 102

HQP4-MVS										64.47 135			88.61 137			84.91 182

HQP3-MVS								83.68 142							73.12 141

HQP2-MVS												37.35 184

NP-MVS					78.76 193	50.43 177					85.12 155

ACMMP++_ref															63.20 215

ACMMP++															59.38 239

Test By Simon												39.38 160

ITE_SJBPF				51.84 319	58.03 338	31.94 333		53.57 338	36.67 327	41.32 314	75.23 266	11.17 338	51.57 343	25.81 326	48.04 305	72.02 319

DeepMVS_CX				13.10 335	21.34 357	8.99 356		10.02 357	10.59 350	7.53 352	30.55 346	1.82 357	14.55 353	6.83 349	7.52 349	15.75 347

High-resolution multi-view benchmark